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Plant scientists around the world share a common goal: understanding plants to improve their tolerance of environmental stresses, resist disease and ultimately, increase yield. Global collaborations that share knowledge and technology are rich in experience and are essential to help accelerate our understanding to meet future challenges.

A recent meeting in El Batán, Mexico, is an excellent example of great minds coming together. Three team members from the Australian Plant Phenomics Facility joined host institution, CSIRO, and CIMMYT in a two-day workshop aimed at achieving critical steps towards a common framework for field phenotyping techniques, data interoperability and sharing experience.

“Capitalising on our respective strengths, we developed basic concepts for several collaborations in physiology and breeding, and will follow up within ongoing projects and through pursuit of new funding,” said Matthew Reynolds, CIMMYT wheat physiologist, signaling the following:

Study of major differences between spike and leaf photosynthesis, and attempts to standardise gas exchange between field and controlled environments.

Work with breeders to screen advanced lines for photosynthetic traits in breeding nurseries, including proof of concept to link higher photosynthetic efficiency/performance to biomass accumulation.

Validation/testing of wheat simulation model for efficient use of radiation.

Evaluation of opportunities to provide environment characterisation of phenotyping platforms, including systematic field/soil mapping to help design plot and treatment layouts, considering bioassays from aerial images as well as soil characteristics such as pH, salinity, and others.

Testing the heritability of phenotypic expression from parents to their higher-yielding progeny in both Mexico and Australia.

Extraction of new remote sensed traits (e.g., number of heads per plot) from aerial images by machine learning (ML) of scored traits by breeders and use of ML to teach those to the algorithm.

Demonstrating a semantic data framework’s use in identifying specific genotypes for strategic crossing, based on phenotypes.

Exchanging suitable data sets to test the interoperability of available data management tools, focusing on the suitability of the Phenomics Ontology Driven Data (PODD) platform for phenotypic data exchanges, integration, and retrieval.

CSIRO and CIMMYT share a long history in crop modelling and physiology, spanning more than 40 years. CIMMYT works throughout the developing world to improve livelihoods and foster more productive, sustainable maize and wheat farming. The centre’s portfolio squarely targets critical challenges, including food insecurity and malnutrition, climate change and environmental degradation. Through collaborative research, partnerships, and training, the centre helps to build and strengthen a new generation of national agricultural research and extension services in maize- and wheat-growing nations. As a member of the CGIAR System composed of 15 agricultural research centres, CIMMYT leads the CGIAR Research Programs on Maize and Wheat, which align and add value to the efforts of more than 500 partners.

Co-tutored by Warren Creemers (Software Team Leader), Vidya Bala (Software Engineer – data) and Robert Fulton (Software Engineer – web development), the team will work to develop a front-end and data annotation tool for the Phenomics Ontology Driven Data and metadata repository, PODD.

The aim of PODD is to provide a search engine and statistical analysis for phenomics experiments, enabling scientists to quickly filter experiments and measurements against large, historical collections from the APPF as well as datasets from the phenomics community. Users will be able to quickly find the specific metadata they are looking for across several experiments and trials, the links to data locations and documentation, and retrieve the datasets they require for validation or testing new analysis techniques from one place without having to repeat experiments or search for missing metadata. PODD also allows users to share their experiments with the phenomics community, organise metadata and experiments in a standardised manner (matching international phenomics standards), and retrieve all the required metadata for publications in a standardised format.

This tool is part of a national initiative to increase the discoverability of data generated within research institutions across Australia to facilitate data re-use.

We wish a warm welcome to ANU TechLauncher students Xiongpan Zhang and Liwei He (preparing a Bachelor of Information Technology, ANU), Haitian Zhang and Yanlin Liu (preparing a Master of Computing, ANU) and Zihao Wang (preparing a Bachelor of Advanced Computing, ANU).

In a recent paper, researchers have developed a methodology suitable for analyzing the growth curves of a large number of plants from multiple families. The corrected curves accurately account for the spatial and temporal variations among plants that are inherent to high-throughput experiments.

An example of curve registration. a The salinity sensitivity (SS) curves of the 16 functions from an arbitrary family, b SS curves after the curve registration, and c the corresponding time-warping functions. The salinity sensitivity on the y-axis of a and b refers to the derivative of the relative decrease in plant biomass

Advanced high-throughput technologies and equipment allow the collection of large and reliable data sets related to plant growth. These data sets allow us to explore salt tolerance in plants with sophisticated statistical tools.

As agricultural soils become more saline, analysis of salinity tolerance in plants is necessary for our understanding of plant growth and crop productivity under saline conditions. Generally, high salinity has a negative effect on plant growth, causing decreases in productivity. The response of plants to soil salinity is dynamic, therefore requiring the analysis of growth over time to identify lines with beneficial traits.

In this paper the researchers, led by KAUST and including Dr Bettina Berger and Dr Chris Brien from the Australian Plant Phenomics Facility (APPF), use a functional data analysis approach to study the effects of salinity on growth patterns of barley grown in the high-throughput phenotyping platform at the APPF. The method presented is suitable to reduce the noise in large-scale data sets and thereby increases the precision with which salinity tolerance can be measured.

Purdue University’s Agronomy and Agricultural and Biological Engineering departments are offering a field-based Phenomics Workshop for crop research professionals involved in predicting yield and characterising biotic and abiotic stress, as well as engineers involved in developing and using sensors and sensor platforms for application.